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Enabling Adversarial Robustness in AI Models through Kubeflow MLOps

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arXiv:2605. 15249v1 Announce Type: new Abstract: AI models are increasingly deployed in cloud-native environments to support scalable and automated services.

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Authors: Stavros Bouras, Ioannis Korontanis, Antonios Makris, Konstantinos Tserpes
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Enabling Adversarial Robustness in AI Models through Kubeflow MLOps overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, rate, project, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.15249

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